Available Projects
EEG signal analysis in the home/ away from the clinic: on the development of robust and efficient data analysis techniques
The electroecephalogram (EEG) is a recording of the electrical activity of the brain. It is usually recorded from multiple recording sites over the scalp - from a few channels to, sometimes, hundred of channels of recordings. Recording robust EEG in the home (away from the clinic) presents unique challenges - this project attempts top address these through robust data analysis techniques.
Primary supervisor: Christopher James - Email: c.james@warwick.ac.uk
Project detail:
Usually the mainstay of the EEG laboratory, due to the use of expensive equipment that needs long preparation times for recordings, techniques that use EEG to assess brain health become limited in both scope and application. However, with the development of portable, more affordable, wireless systems that incorporate new-technologies such as the use of dry-electrodes, it is now possible to record multi-channel EEG from a variety of end-users in the home/community. This opens up the possibilities for using EEG in a much wider scope than before - making EEG analysis available to a wider group of potential end-users, that usually cannot travel to the clinic. Equally, this means that EEG can be gathered "longitudinally" - i.e. over an extended period of time, over many sessions in the home or community.
This project will look at using a multi-channel portable EEG system in the home / community and will work on developing robust signal processing algorithms to extract measures of interest from the EEG over a number of data collection sessions over time. The data will be collected from two groups: a) from a "control" group of healthy volunteers which will use the EEG system away from the clinic to follow and develop a "brain training" regime, such as the so-called motor-imagery paradigm in brain-computer interfacing and b) in a group which have Intellectual Difficulties and are diagnosed as epileptic. In the latter group, the challenge is to extract clean EEG that can be used to automatically detect and extract epileptiform data routinely, in a group which do not usually have routine access to EEG and thus are underserved.
The project will look at the development of data processing algorithms, building on previous research, that can undertake de-noising of the data and extraction of the signals of interest through techniques such as extended blind source separation and other similar component based methods.
The project will incorporate the involvement of clinicians working in the field of epilepsy.
Usually the mainstay of the EEG laboratory, due to the use of expensive equipment that needs long preparation times for recordings, techniques that use EEG to assess brain health become limited in both scope and application. However, with the development of portable, more affordable, wireless systems that incorporate new-technologies such as the use of dry-electrodes, it is now possible to record multi-channel EEG from a variety of end-users in the home/community. This opens up the possibilities for using EEG in a much wider scope than before - making EEG analysis available to a wider group of potential end-users, that usually cannot travel to the clinic. Equally, this means that EEG can be gathered "longitudinally" - i.e. over an extended period of time, over many sessions in the home or community.
This project will look at using a multi-channel portable EEG system in the home / community and will work on developing robust signal processing algorithms to extract measures of interest from the EEG over a number of data collection sessions over time. The data will be collected from two groups: a) from a "control" group of healthy volunteers which will use the EEG system away from the clinic to follow and develop a "brain training" regime, such as the so-called motor-imagery paradigm in brain-computer interfacing and b) in a group which have Intellectual Difficulties and are diagnosed as epileptic. In the latter group, the challenge is to extract clean EEG that can be used to automatically detect and extract epileptiform data routinely, in a group which do not usually have routine access to EEG and thus are underserved.
The project will look at the development of data processing algorithms, building on previous research, that can undertake de-noising of the data and extraction of the signals of interest through techniques such as extended blind source separation and other similar component based methods.
The project will incorporate the involvement of clinicians working in the field of epilepsy.
The University of Warwick provides an inclusive working and learning environment, recognising and respecting every individual’s differences. We welcome applications from individuals who identify with any of the protected characteristics defined by the Equality Act 2010.